BICM decoding in the presence of co-channel interference

ABSTRACT

Systems and methods are provided for computing soft information for digital information based on a received signal, where the received signal suffers from noise and interference. A receiver that decodes the received signal may estimate channel information, such as the channel gain, associated with the interfering source. The receiver may also obtain modulation information through a backbone network or by decoding control information transmitted by the interfering source. Using the modulation information and the channel information, the receiver may estimate the effect that interference has on the received signal, and may compute soft information (e.g., a log-likelihood ratio) for the digital information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/119,264, filed May 12, 2008 (currently pending), which claims thebenefit under 35 U.S.C. §119(e) of U.S. Provisional Application No.60/917,433 filed May 11, 2007, which are hereby incorporated herein byreference in their entireties.

BACKGROUND OF THE DISCLOSURE

The disclosed technology relates generally to decoding received signals,and more particularly to computing soft information for informationreceived from an intended source in the presence of interference fromother sources.

There are several known wireless protocols for cellular and Internetsystems. These wireless protocols attempt to provide high transmissionreliability to wireless users, such as cellular telephone users, toprevent dropped telephone calls or poor voice transmissions. Forexample, to reduce the effect of interfering signals, the Global Systemsfor Mobile communications (“GSM”) protocol decomposes the frequency bandallocated for cellular communication into seven frequency channels. Thisallows a cellular telephone to tune into only the appropriate channel toavoid interfering signals that are transmitted through the other sixchannels. However, such a communications technique forces datatransmission to occur at a fraction of the maximum possible bandwidth.Reducing the bandwidth in this manner limits the maximum data rate thatcan be achieved by a communications network.

SUMMARY OF THE DISCLOSURE

Accordingly, systems and methods are disclosed for computing softinformation in the presence of interfering signals. These systems andmethods enable wireless communication to occur without having todecompose the frequency spectrum into different frequency transmissionchannels.

The embodiments of the present invention can be employed in any suitablewireless communications system, such as a cellular system (e.g., amobile network) or a wireless Internet system (e.g., a WiMAX network).Using a cellular system as an example, the cellular system may include aplurality of base stations that can each communicate with mobilestations (e.g., cellular telephones) that are within an area assigned tothat base station. When a mobile station is connected to the cellularsystem, however, the mobile station may receive radio signals from notonly an intended source (e.g., the base station assigned to cover thearea that the mobile station is located in), but from one or moreinterfering sources (e.g., neighboring base stations transmitting datato other mobile stations). Thus, the mobile station may be configured todecode a received signal in a manner that takes into account not onlycharacteristics of the intended source, but also any interferingsources.

The mobile station may estimate channel information associated with theone or more interferences sources. The channel information may allow themobile station to determine how much of an effect that the interferingsources can have on a received signal. For example, the mobile stationcan estimate interference channel by analyzing a pilot signal receivedfrom an interfering source. From the pilot signal, the mobile stationmay determine an interference channel gain (e.g., magnitude and phase ofthe gain) associated with the physical space between the mobile stationand the interfering source. Alternatively, the mobile station maycompute just magnitude information for the interference channel gain,such as an average magnitude square or an instantaneous magnitude squareof the interference channel gain.

The mobile station may also identify modulation information associatedwith each of the interfering sources (e.g., base stations). The mobilestation may be able to interpret control information broadcasted fromthe interfering sources to determine what type of modulation scheme thateach interfering source uses. For example, in WiMAX systems, each basestation can transmit control information in the form of a DL-MAP messagethat the mobile station can interpret.

Thus, the mobile station may have both channel information andmodulation information from each of the interfering sources (e.g., basestations) affecting a received signal. Using the channel information andmodulation information, the mobile station can compute soft informationfor the information transmitted by the intended source. The softinformation may be in the form of a log-likelihood ratio (LLR), forexample. The mobile station can compute the LLR for the intendedinformation by treating each received signal as a combination of anintended signal and an interference signal. That is, the mobile stationdoes not assume that the interference can be modeled as random noise,and instead estimates the actual affect that interference signal canhave on the received signal. For example, the mobile station may operateusing a signal constellation set that incorporates the magnitude and/orphase information for both the signal constellation set of the intendedsignal and the signal constellation set of the interference signal.

BRIEF DESCRIPTION OF THE FIGURES

The above and other aspects and advantages of the invention will beapparent upon consideration of the following detailed description, takenin conjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 is a diagram of three radio cells of an illustrative cellularsystem;

FIG. 2 is a block diagram of an illustrative base station transmitter;

FIG. 3 is a block diagram of an illustrative mobile station receiver;

FIG. 4 is a signal constellation set for a QPSK modulation scheme thatillustrates signals that may be transmitted by a base stationtransmitter;

FIG. 5 is a signal constellation set for a QPSK modulation scheme thatillustrates signals that may be received by a mobile station receiver;

FIG. 6 is a signal constellation set for a QPSK modulation scheme thatillustrates an intended signal that may be transmitted by a base stationtransmitter;

FIG. 7 is a signal constellation set for a QPSK modulation scheme thatillustrates an interference signal that may be transmitted by a basestation transmitter;

FIGS. 8-9 are signal constellation sets illustrating signal points thatmay be received by a mobile station receiver from a combination of theintended signal and the interference signal of FIGS. 6 and 7,respectively;

FIGS. 10A-10B shows flow diagrams of illustrative processes forestimating an intended signal in the presence of inter-cell co-channelinterference;

FIG. 11 is a block diagram of an exemplary hard disk drive that canemploy the disclosed technology;

FIG. 12 is a block diagram of an exemplary digital versatile disc thatcan employ the disclosed technology;

FIG. 13 is a block diagram of an exemplary high definition televisionthat can employ the disclosed technology;

FIG. 14 is a block diagram of an exemplary vehicle that can employ thedisclosed technology;

FIG. 15 is a block diagram of an exemplary cell phone that can employthe disclosed technology;

FIG. 16 is a block diagram of an exemplary set top box that can employthe disclosed technology; and

FIG. 17 is a block diagram of an exemplary media player that can employthe disclosed technology.

DETAILED DESCRIPTION OF THE DISCLOSURE

FIG. 1 shows a simplified diagram of illustrative cellular system 100.Cellular system 100 can include a plurality of base stations that areinterconnected to form a mobile or cellular network. These base stationscan include base stations 122, 142, and 162. Each of these base stationscan be configured to communicate with mobile stations located within aparticular physical area within that base station's radio communicationsrange. The physical area may be referred to as a radio cell. Inparticular, base station 122 may communicate with mobile stations withinradio cell 120, base station 142 may communicate with mobile stationswithin radio cell 140 (e.g., mobile stations 144 and 146), and basestation 162 may communicate with mobile stations within radio cell 160(e.g., mobile station 164). In FIG. 1, radio cells 120, 140, and 160 arerepresented by hexagonal regions, although this shape is merelyillustrative.

Mobile stations 144, 146, and 164 may be any suitable type of cellulartelephone compatible with the base stations of the mobile network. Forexample, mobile stations 144, 146, and 164 can operate based on aprotocol or communications standard compatible with base stations 122,142, and 162. The base stations and mobile stations of cellular system100 can operate using any suitable conventional cellular protocol, suchas the Global Systems for Mobile communications (“GSM”) standard or thecode division multiple access (“CDMA”) standard, or using anon-conventional protocol.

The base stations and mobile stations in cellular system 100 may use anyof a variety of modulation and coding schemes to enable reliablecommunication. For example, base stations 122, 142, and 162 may operatewith a modulation scheme based on orthogonal frequency divisionmultiplexing (“OFDM”). Further examples of suitable modulation andcoding schemes will be discussed in detail below in connection withFIGS. 2 and 3. To notify the mobile stations of the modulation andcoding used by a base station, base stations 122, 142, and 162 maybroadcast a control sequence to at least the mobile stations withintheir respective radio cells. In addition to coding and modulationinformation, the control sequence may also include any other suitablecontrol information that the mobile stations may use to interpret thedata sent by a base station. For example, the control sequence mayinclude information on how the data frames are structured, how manysymbols are included in each frame, and the intended recipient (e.g.,mobile station) of the next data block.

Base stations 122, 142, and 162 may also transmit a pilot signal to eachmobile station within its radio cell to provide each mobile stationwith, among other things, phase alignment information. The pilot signalmay be modulated by a particular pseudo-noise (“PN”) sequence, and eachbase station may utilize a different PN sequence. The different PNsequences may allow the mobile stations (e.g., mobile station 144) toidentify the base station associated with a received pilot signal.

Base stations 122, 142, and 162 may broadcast a pilot signal, controlinformation, and network data to all mobile stations that are withinradio communication range. This allows each base station to not onlytransmit information to any mobile station within that base station'sradio cell, but also to mobile stations in neighboring radio cells thatare sufficiently close to the base station. For example, due to theproximity of mobile station 144 to base station 142 in radio cell 140,mobile station 144 may predominantly receive information from basestation 142. Mobile station 146, on the other hand, may be able toreceive information not only from base station 142 in radio cell 140,but may also receive interfering information from base station 162 inneighboring radio cell 160. If base stations 142 and 162 operate usingthe same frequency band or channel such that signals received from thesetwo sources are not easily distinguishable, mobile station 146 maysuffer from an effect referred to sometimes as “inter-cell co-channelinterference” (or simply “co-channel interference” or “interference”).

For simplicity, the radio signal expected by mobile station 146 (e.g.,from base station 142, or the “intended source”) may sometimes bereferred to as the “intended signal,” and the channel gain of thecorresponding channel (e.g., the “intended channel”) may sometimes bereferred by the symbol, h_(k). The radio signal from a neighboringmobile station (e.g., from base station 162, or the “interferingsource”) may sometimes be referred to as the “interference signal,” andthe channel gain of the corresponding channel (e.g., the “interferencechannel”) may sometimes be represented by the symbol, g_(k).

In many scenarios, the co-channel interference (e.g., the effect of basestation 162 on mobile station 146) may be stronger than any noise thatmay occur during data transmission from base station to mobile station.This may be especially true when a mobile station is near the boundaryof two radio cells. In current communications protocols, such as GSM,co-channel interference is circumvented by having neighboring basestations broadcast network data using different frequency channels. Forexample, if cellular system 100 were to operate using one of thesecurrent protocols, the mobile network can assign a first frequencychannel to base station 122 and radio cell 120, a second frequencychannel to base station 142 and radio cell 140, and a third frequencychannel to base station 162 and radio cell 160. By having neighboringbase stations use different frequency channels, a mobile station in aparticular radio cell can suffer from little to no interference from abase station in a neighboring radio cell. For example, in this scenario,even though mobile station 146 can be able to receive an interferencesignal from neighboring base station 162, mobile station 146 can tuneinto only the frequency channel of base station 142 to ensure that radiosignals from base station 162 are substantially excluded.

In some embodiments, each radio cell of cellular system 100 may befurther broken up into physical regions referred to sometimes assectors, and current protocols can assign each of the sectors adifferent frequency channel. Radio cells may be decomposed into anysuitable number of sectors (e.g., 2-10 sectors). For example, radio cell120 may be decomposed into three sectors: sector 130, sector 132, andsector 134. Likewise, radio cell 140 may be decomposed into sector 150,sector 152, and sector 154 and radio cell 160 may be decomposed intosector 170, 174, and 176. In current protocols, each of these sectorsmay be assigned to a different or the same frequency channel by themobile network. For example, the mobile network may assign each of thethree sectors in radio cells 120, 140, and 160 to different frequencychannels such that no neighboring sector uses the same frequencychannel. As with the example above, where each radio cell is assigned toa different frequency, this scenario also allows the mobile stations todecode received signals without concern for interference effects.

The communications technique of assigning neighboring base stations orsectors different frequency bands may be referred to as frequency reuse.Cellular system 100 may, as described above, use three differentfrequency channels to implement frequency reuse. Such a communicationssystem may be referred to as having a frequency reuse of 3 or ⅓. GSMillustrates one protocol that can, in some embodiments, be implementedby the mobile network of cellular system 100. GSM uses seven differentfrequency channels and therefore has a frequency reuse of 7 or 1/7.

While frequency reuse ensures that mobile stations will not suffer frommuch interference, frequency reuse does not efficiently utilize thebandwidth made available to cellular systems. That is, cellular systemsare assigned a limited amount of bandwidth. With each base station usingonly a fraction of the available bandwidth, each base station has aspectral efficiency (and therefore a maximum data rate) that is wellbelow the possible spectral efficiency and data rate that can beachieved. Accordingly, embodiments of the present invention includetechniques that enable a frequency reuse of one. In particular,embodiments of the present invention advantageously provide techniquesthat can counter the effects of inter-cell co-channel interference suchthat using different frequency channels in neighboring radio cells orsectors is unnecessary.

Thus, in some embodiments, base stations 122, 142, and 162 may transmitdata to mobile stations using up to the full frequency band available tothe mobile network. To ensure reliability in communicating the controlmessage, which in turn allows a mobile station to accurately interpretdata, the control message may be transmitted with frequency reuse. Forthe example of FIG. 1, base stations 122, 142, and 162 may operate usinga frequency reuse of 3 or ⅓ when transmitting control information andmay operating using a frequency reuse of one when transmitting data.This example applies to WiMax systems, which, if implemented here, maytransmit control information referred to as a DL-MAP message with afrequency reuse of 3 or ⅓. A transmission scheme that uses frequencyreuse only when transmitting control information may be advantageous, asreliability in communicating the control message is maintained withoutconcern for inter-cell co-channel interference, while data (which canconstitute the majority of the information transmitted from a basestation) is transmitted with high spectral efficiency and data rate.

While some embodiments of the present invention are described in termsof a mobile station that receives intended and interfering informationfrom various base stations, this is merely to simplify the descriptionof the present invention. These embodiments may also be used to allow abase station to handle intended and interfering information receivedfrom various mobile stations. That is, some or all of the embodimentsdescribed herein for the downlink scenario may also be applied to theuplink scenario. Also, the present invention may be implemented not onlyin cellular systems, but in any application that may suffer frominter-cell co-channel interference.

FIG. 2 shows a simplified block diagram of base station transmitter 200that can prepare network information 210 for transmission as radiosignal 270. In some embodiments, base station transmitter 200 may beimplemented as the transmitter for one or more of base stations 122,142, and 162 of FIG. 1. Base station transmitter 200 can include encoder220, bit-interleaver 240, and Gray mapper/modulator 260.

Encoder 220 may encode network information 210 based on a suitable errorcorrecting code (“ECC”). For example, encoder 220 may operate using aconvolutional code (e.g., a rate-½ or rate-⅔ convolutional code) ofmemory m. Encoder 220 may therefore convert network information 210,which may be some form of digital information (e.g., a stream of binarydata), into an encoded stream of binary data. Since encoder 220 may havea memory of m, each m consecutive bits in the encoded stream created byencoder 220 depends on the value of the same one bit of networkinformation 210. In order to remove any negative effects that may resultfrom this dependency (e.g., the inability to reliably decode when bursterrors are present), the encoded stream may be interleaved bybit-interleaver 240. In particular, bit-interleaver 240 may change theorder of the bits in the encoded stream to ensure that neighboring bitsin the interleaved sequence are effectively independent of each other.

Gray mapper/modulator 260 of base station transmitter 200 may beconfigured to convert the interleaved digital sequence produced bybit-interleaver 240 into a signal for transmission. Graymapper/modulator 260 may first group bits of the interleaved sequenceinto symbols based on the size of a modulation scheme, and may thenmodulate the symbols into a signal having a particular magnitude andphase specified by the modulation scheme. Gray mapper/modulator 260 mayuse any suitable modulation scheme of any of a variety of sizes. Forexample, Gray mapper/modulator 260 may utilize a quadrature amplitudemodulation (“QAM”) scheme (e.g., 4QAM, 16QAM, 32QAM) or a phase shiftkeying (“PSK”) modulation scheme (e.g., QPSK, 16PSK, 32PSK).

The particular modulation scheme employed by Gray mapper/modulator 260may be designed to operate effectively with the particular errorcorrecting code (ECC) employed by encoder 200. This type ofcommunications technique is commonly referred to as coded modulation.Therefore, as base station transmitter 200 of FIG. 2 also includesbit-interleaver 240, the overall communications technique employed bybase station transmitter 200 can be referred to as bit-interleaved codedmodulation (“BICM”).

The modulation scheme used by Gray code mapper/modulator 260 may beassociated with a signal constellation set that defines the magnitudeand phase of a carrier signal that is transmitted for each possiblesymbol value. For example, FIG. 4 shows an illustrative signalconstellation set 400 for a 4QAM/QPSK system, where each “X” representsa signal constellation point having a particular phase and magnitude.For example, signal constellation point 420 has a magnitude of one and aphase of −45 degrees. Thus, when that signal constellation point isselected for transmission, Gray mapper/modulator 260 may produce a radiosignal that has a magnitude of one and a phase of −45 degrees.

Each signal constellation point in signal constellation set 400 isassociated with a particular two-bit symbol. The symbols may be assignedto the signal constellation points based on a Gray code mapping. Graycode mapping maps neighboring signal points in the modulation scheme tosymbols that differ in only one bit. For example, in FIG. 4, the twosignal points that correspond to symbols differing by two bits (“00” and“11”) are not neighboring signal points. Gray code mapping thereforeensures that, even if a signal were mistaken for a neighboring signalpoint when decoded, the incorrectly decoded signal can be incorrect inonly one bit.

Returning to FIG. 2, Gray code mapper/modulator 260 may produce radiosignal 270 for transmission to one or more mobile stations (e.g., mobilestations 144, 146, or 162). Radio signal 270 may sometimes berepresented by the variable, x. At some time, k, radio signal 270 mayrepresent a symbol of encoded/interleaved network information 210, andat some time, k+1, radio signal 270 may represent the next symbol ofencoded/interleaved network information 210. For simplicity, thevariable x_(k) will be used below to represent the value of radio signal270 when sampled at a particular time, k. In some embodiments, krepresents another type of dimension of radio signal 270 other thantime, such as a spatial dimension or frequency dimension.

Radio signal 270 may be subject to noise (e.g., random noise orsignal-dependent noise) during data transmission from base stationtransmitter 200 to a mobile station. In some scenarios, radio signal 270may also be subject to co-channel interference that further distortsradio signal 270. Thus, even though radio signal 270 is transmitted, theradio signal actually received by a mobile station receiver may beconsiderably different from radio signal 270.

FIG. 3 shows a simplified block diagram of mobile station receiver 300.In some embodiments, mobile station receiver 300 may be implemented aspart of one or more mobile stations 144, 146, and 164. Mobile stationreceiver 300 can be configured to receive and decode a noisy ordistorted version of radio signal 270 (FIG. 2). In particular, mobilestation receiver 300 may receive radio signal 370, which may be radiosignal 270 after being affected by random or signal-dependent noise andinter-cell co-channel interference. Radio signal 370 may sometimes berepresented by the variable, y_(k) for some time, k. Mathematically,radio signal 370 may be given by,y _(k) =h _(k) x _(k) +v _(k).  (EQ. 1)In EQ. 1, h_(k) is the channel gain that represents the magnitude andphase effect of the intended channel, and v_(k) may represent both thenoise and interference affecting radio signal 270.

Since v_(k) in EQ. 1 may be a combination of noise and interference, EQ.1 may be re-written as,y _(k) =h _(k) x _(k) +w _(k) +z _(k),  (EQ. 2)where z_(k) constitutes the noise component of v_(k), and w_(k)constitutes the interference component of v_(k). Finally, as theinterference signal may be associated with an interference channel gain,g_(k) (as described above in connection with FIG. 1), EQ. 2 may berewritten as,y _(k) =h _(k) x _(k) +g _(k) s _(k) +z _(k).  (EQ. 3)Here, s_(k) may be a radio signal that represents a symbol that theinterfering base station intends to transmit to a different mobilestation. Note that s_(k) may be associated with a modulation scheme witha different number of signal constellation points, of differingmagnitudes, and with a different symbol-to-signal point mapping.

Mobile station receiver 300 can be configured to decode radio signal 370and obtain an estimate of the originally transmitted information (e.g.,network information 210 of FIG. 2). To decode radio signal 370, mobilestation receiver 300 can include soft bit-metric calculator 360,de-interleaver 340, and decoder 320. Each of these receiver componentsmay correspond to a transmitter component in base station transmitter200 and may effectively undo the operation performed by thecorresponding transmitter component. For example, soft bit-metriccalculator 360 may correspond to Gray mapper/modulator 260 that candemodulate/de-map radio signal 370 using at least the modulation schemeand signal constellation set as Gray mapper/modulator 260.De-interleaver 340 may correspond to bit-interleaver 240 and may returnthe order of the received data into its original order, e.g., the orderexpected by decoder 320. Decoder 320 may be a soft-decoder thatcorresponds to encoder 220, and may perform decoding based on the sameerror correcting code (e.g., convolutional code) as encoder 220. Thus,decoder 320 may produce estimate 310 of network information (e.g.,network information 210). In some embodiments, decoder 320 may be aViterbi decoder or a Turbo decoder. If mobile station 300 successfullyinterprets radio signal 370, estimate 310 may be the same digitalsequence as network information 210.

Referring to soft bit-metric calculator 360 of FIG. 3 in more detail,soft bit-metric calculator 360 may calculate soft information for eachbit of information contained within radio signal 370. The softinformation may be in the form of a log-likelihood ratio (“LLR”) foreach received bit. Alternatively, the soft information can beproportional to an LLR. Soft bit-metric calculator 360 may calculate anLLR according to EQ. 4:

$\begin{matrix}{L\; L\;{R\left( {{{b_{i}\left. y_{k} \right)} = {\log\frac{\left. {{\Pr\left( {b_{i} = 0} \right.}y_{k}} \right)}{\Pr\left( {b_{i} = {1\left. y_{k} \right)}} \right.}}},} \right.}} & \left( {{EQ}.\mspace{14mu} 4} \right)\end{matrix}$where b_(i) is the transmitted bit contained within y_(k) for which theLLR is being calculated. Soft bit-metric calculator 360 can obtain areliable log-likelihood ratio based on EQ. 4 by using accurate estimatesof the channel information for both the intended channel and theinterference channel and modulation information. Embodiments of mobilestation receiver 300, and in particular soft bit-metric calculator 360,that can obtain reliable LLRs even in the presence of interference aredescribed below in connection with EQ. 12 through EQ. 16 and FIGS. 6-10.However, to illustrate the advantages and improvements of theseembodiments, and to simplify their description below, conventionalimplementations of soft bit-metric calculator 360 are first described.

Conventionally, soft bit-metric calculators are implemented assumingthat any noise affecting y_(k) is additive white Gaussian noise(“AWGN”). That is, conventional implementations of soft bit-metriccalculator 360 assume that the probability distribution function of anynoise (e.g., random noise or co-channel interference) is accuratelyreflected by:

$\begin{matrix}{{{A\; W\; G\; N_{{PDF}{y_{k}}}} = {\frac{1}{\sigma\sqrt{2\pi}}{\exp\left( {- \frac{{{y_{k} - {E\left\lbrack y_{k} \right\rbrack}}}^{2}}{2\sigma^{2}}} \right)}}},} & \left( {{EQ}.\mspace{14mu} 5} \right)\end{matrix}$and calculate the LLR equation of EQ. 4 according to,

$\begin{matrix}{{LLR}_{i} = {{\log\left( {\sum\limits_{x \in X_{l_{i}}^{(1)}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{v}^{2}}} \right)}} \right)} - {\log\left( {\sum\limits_{x \in X_{l_{i}}^{(0)}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{v}^{2}}} \right)}} \right)}}} & \left( {{EQ}.\mspace{14mu} 6} \right)\end{matrix}$for each valid value of the transmit signal, x. In EQ. 6 and otherequations below, X_(i) ^((j)) is the set of symbols that have a bitvalue of j at bit position b_(i), and σ_(v) ² is the power of the noiseand interference, v_(k). The values used in EQ. 6 for the intendedchannel gain and noise and interference power may be estimates computedby the mobile station or predetermined values. In other conventionalimplementations, interference is not considered at all, and the noiseand interference power in EQ. 6 is replaced by just the noise power,σ_(z) ².

Instead of computing EQ. 6, an approximation can be implemented tosimplify the complexity of the hardware (e.g., logic) or software. Forexample, a conventional implementation of soft bit-metric calculator 360may employ an approximation for computing logarithms, and can insteadcalculate,

$\begin{matrix}{{LLR}_{i,{approx}} = {{\frac{1}{\sigma_{v}^{2}}\left\lbrack {{\min\limits_{x \in X_{l_{i}}^{(0)}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x}}}^{2} \right\}} - {\min\limits_{x \in X_{l_{i}}^{(1)}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x}}}^{2} \right\}}} \right\rbrack}.}} & \left( {{EQ}.\mspace{14mu} 7} \right)\end{matrix}$Note that EQ. 7, unlike EQ. 6, advantageously does not includepotentially resource-intensive exponential or logarithm computations.Moreover, EQ. 7 ultimately uses only two possible values of x (a firstvalue with i=0 and a second value with i=1) to compute the approximateLLR, and not all of the possible values of x.

To improve the performance of computing soft bit-metrics, someconventional soft bit-metric calculators adaptively update the estimateof the noise and interference power, σ_(v) ², such that an accurateestimate is maintained. That is, rather than using a fixed value for thenoise and interference power as in EQ. 6 and EQ. 7, a conventionalimplementation of soft bit-metric calculator 360 treats the noise andinterference power as being a function of k. One conventionalimplementation of soft bit-metric calculator 360 therefore computes, inplace of EQ. 6 or EQ. 7, either:

$\begin{matrix}{{{{LLR}_{i} = {{\log\left( {\sum\limits_{x \in X_{l_{i}}^{(1)}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{v_{k_{i}}}^{2}}} \right)}} \right)} - {\log\left( {\sum\limits_{x \in X_{l_{i}}^{(0)}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{v_{k_{i}}}^{2}}} \right)}} \right)}}}\mspace{79mu}{or}}\mspace{265mu}} & \left( {{EQ}.\mspace{14mu} 8} \right) \\{{LLR}_{i,{approx}} = {{\frac{1}{\sigma_{v_{k_{i}}}^{2}}\left\lbrack {{\min\limits_{x \in X_{l_{i}}^{(0)}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x}}}^{2} \right\}} - {\min\limits_{x \in X_{l_{i}}^{(1)}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x}}}^{2} \right\}}} \right\rbrack}.}} & \left( {{EQ}.\mspace{14mu} 9} \right)\end{matrix}$

The LLR equations of EQS. 6-9 are based on Euclidean distancecalculations between radio signal 370 and the signal that can have beenreceived had the transmitted radio signal (e.g., radio signal 270) notbe subjected to noise and/or interference. The distance calculations maybe visualized from a signal constellation set used by soft bit-metriccalculator 360. FIG. 5 shows such a signal constellation set for a 4QAMscheme that can be used by a soft bit-metric calculator that implementsany of EQ. 6 through EQ. 9 above. Signal constellation set 500 may beassociated with signal constellation set 400. That is, a receiver thatoperates based on signal constellation set 500 of FIG. 5 may beassociated with a transmitter that operates based on signalconstellation set 400, where each signal point in set 500 represents themagnitude and phase of a signal received by the receiver had a signalwith the magnitude and phase of a signal point in set 400 beentransmitted without noise or interference. For example, as illustratedin FIGS. 4 and 5, signal point 510 in set 500 may have a magnitudesubstantially equal to the channel gain magnitude (e.g., |h_(k)|) whencorresponding signal point 410 in set 400 has a magnitude of one.

When interference and/or noise is introduced, a sampled received signal(e.g., radio signal 370 of FIG. 3) that corresponds to a transmittedsignal (e.g., radio signal 270 of FIG. 2) of signal point 420 (FIG. 4)may have a magnitude and phase of signal point 525. To compute alog-likelihood ratio according to any of EQ. 6 through EQ. 9, softbit-metric calculator 360 (FIG. 3) may computes the Euclidean distancebetween each constellation point in set 500 to signal point 525. In theillustrative scenario of FIG. 5, after computing each distance, theconventional implementation of soft bit-metric calculator 360 maycalculate the LLR for the most significant transmitted bit (bit 1)according to,

$\begin{matrix}{{{LLR}_{1,{approx}} = {\frac{1}{\sigma_{v}^{2}}\left\lbrack {{D\; 0^{2}} - {D\; 1^{2}}} \right\rbrack}}{or}} & \left( {{EQ}.\mspace{14mu} 10} \right) \\{{LLR}_{1,{approx}} = {\frac{1}{\sigma_{v_{k_{i}}}^{2}}\left\lbrack {{D\; 0^{2}} - {D\; 1^{2}}} \right\rbrack}} & \left( {{EQ}.\mspace{14mu} 11} \right)\end{matrix}$EQ. 10 represents the equation used when conventional soft bit-metriccalculator 360 operates based on EQ. 7, and EQ. 11 represents theequation used when conventional soft bit-metric calculator 360 operatesbased on EQ. 9.

As described above, EQ. 6 through EQ. 11 represent conventional LLRequations that operate under the assumption that both the noise and anyinterference affecting the received signal can be modeled as AWGN.However, as described above, the interference affecting a receivedsignal in a cellular or WiMAX system (or in another type of system) maybe considerable and may not be represented accurately using thissimplification. Therefore, embodiments of the present invention areprovided that can more accurately model interference signals and usethis information to compute reliable soft metrics.

Mobile station receiver 300 of FIG. 3 can compute soft information forreceived signal 370 using accurate channel and modulation informationfor the interfering source. Using more than just the power of the noiseand interference, soft bit-metric calculator 360 can compute aconsiderably more reliable and accurate log-likelihood ratio or othersoft metric. To compute the channel information estimate, mobile stationreceiver 300 may, for example, include computational logic (not shown)that is configured to estimate the interference channel gain. Thecomputational logic may also be configured to compute the intendedchannel gain. The computational logic can compute these channelinformation estimates by analyzing the characteristics of pilot signalsreceived from each source. Because each source modulates the pilotsignal based on a unique PN sequence, the computational logic candistinguish between the different pilot signals. From the analysis ofvarious pilot signals, the computational logic produces an estimate ofthe interference and/or intended channel gain, for example. Mobilestation receiver 300 may compute the channel information estimates atany suitable time during operation, such as at power-up, when initiallyconnected to a base station, periodically, whenever the pilot signal istransmitted, etc. Channel information estimates can be computed in thismanner for embodiments where receiver 300 is implemented on a mobilestation and for embodiments where receiver 300 is implemented on a basestation.

Mobile station receiver 300 may also identify modulation informationassociated with the interfering source. For example, in a WiMAX system,receiver 300 may include a DL-MAP decoder (not shown) that decodes aDL-MAP message received. From the DL-MAP message, mobile stationreceiver 300 can retrieve the modulation information. As describedabove, the modulation information may include the modulation scheme(e.g., QAM, PSK, PAM), the size of the modulation scheme, and themagnitude/phase associated with the modulation scheme. Since a DL-MAPmessage or other control message may be heavily encoded and may betransmitted using frequency reuse, the receiver may be able toaccurately decode the control information from the interfering sourceeven if noise and/or interference prevents receiver 300 from accuratelydecoding regular data from the interfering source.

For embodiments where soft information is being calculated by a basestation, the base station may obtain modulation information by decodinga DL-MAP type of control message, as described above, or through abackbone network. In particular, for the DL-MAP case, the base stationcan obtain modulation information for an interfering mobile station(located in the radio cell of a neighboring base station) by decoding aDL-MAP message received from the neighboring base station. In otherembodiments, the base station may communicate with the neighboring basestation through the backbone network or backbone link, which establishesa path between various parts or sub-networks of the cellular system(e.g., between two base stations). That is, the base station can requestmodulation information from the neighboring base station through thebackbone network.

With a relatively accurate estimate of the channel information andmodulation information associated with the interfering source, the softbit-metric calculator (e.g., soft bit-metric calculator 360) may computea log-likelihood ratio according to,

$\begin{matrix}{{LLR}_{i} = {{\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)} - {\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)}}} & \left( {{EQ}.\mspace{14mu} 12} \right)\end{matrix}$or an approximation to EQ. 12, e.g.,

$\begin{matrix}{{LLR}_{i} = {\frac{1}{\sigma_{z}^{2}}\left\lbrack {{\min\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2} \right\}} - {\min\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2} \right\}}} \right\rbrack}} & \left( {{EQ}.\mspace{14mu} 13} \right)\end{matrix}$

Compared to EQ. 6 through EQ. 11, EQ. 12 and EQ. 13 do not treat theinterference as random noise (e.g., AWGN with a distribution given byEQ. 5). Rather, EQ. 12 and EQ. 13 explicitly use the channel informationand modulation information associated with the interfering source (e.g.,the interference channel gain) to determine the signal values thatmobile station receiver 300 should expect to receive when interferenceis present. That is, rather than expecting to receive the intendedsignal, soft bit-metric calculator 360 alters the soft informationcomputation such that mobile station receiver 300 expects to receive acombination of the intended signal and the interference signal. Twoexamples of the operation of soft bit-metric calculator 360 (FIG. 3)using EQ. 12 or EQ. 13 will be described below in connection with FIGS.6-9.

FIGS. 8 and 9 illustrate two operating scenarios when mobile stationreceiver 300 receives a signal resulting from a transmitted intendedsignal of signal point 625 of FIG. 6 and a transmitted interferencesignal of signal point 725 of FIG. 7. In particular, FIGS. 8 and 9correspond to illustrative situations when a “01” symbol is transmittedusing 4QAM or QPSK with a magnitude of one and a “00” symbol istransmitted using 4QAM or QPSK also with a magnitude of one. Althoughthese figures use a 4QAM/QPSK modulation, this is merely illustrative,and any other suitable modulation scheme may be used instead.

For clarity, in FIGS. 6-9, signal points associated with an intendedsignal are indicated by an “X” and signal points associated with aninterference signal are indicated by a “+.” Also, for clarity, eachsignal constellation point in FIG. 6 is outlined by a different shape (acircle, a square, diamond, or a triangle) to better differentiate thesignal points. This approach is also used to FIGS. 8 and 9, which showmore complicated signal constellation sets, to clearly demonstrate whichsignal points are associated with which intended signal.

Referring first to FIG. 8, FIG. 8 illustrates the operation of softbit-metric calculator 360 when the interference channel gain is smallerthan the intended channel gain. Since soft bit-metric calculator 360operates based on the expected value of two signals, each with fourpossible values, soft bit-metric calculator 360 decodes based on asignal constellation set with sixteen (or 2^4) signal points (ratherthan four signal points, as was the scenario described above inconnection with FIG. 5). Each signal constellation point is effectivelya vector summation of the expected value of a possible intended signaland the expected value of a possible interference signal. For example,the signal point in set 800 corresponding to a transmitted signalassociated with signal point 625 (FIG. 6) and signal point 725 (FIG. 7)is signal point 810. Thus, each interference signal point (e.g.,indicated with a “+”) in FIG. 8 represents a different one of thesixteen signal points used by soft bit-metric calculator 360 to computelog-likelihood ratios. Note that the receiver is able to obtain theinterference signal points, since the receiver has knowledge of both thechannel gain and the modulation scheme associated with the interferingsource.

Soft bit-metric calculator 360 may calculate either EQ. 12 or EQ. 13 (orboth) using the sixteen “+” signal points of signal constellation set800 of FIG. 8. For example, if a received signal corresponding to signalpoint 825 is received, soft bit-metric calculator 360 may compute thesquared Euclidean distance between signal point 825 and each of thesixteen signal points. All of these squared distance computations may beused to compute the LLR equation of EQ. 12. Alternatively, only twoEuclidean distances may be included in the LLR computation of EQ. 13.Here, soft bit-metric calculator 360 can identify the smallest squaredEuclidean distance between signal point 825 and a signal constellationpoint associated with an intended symbol with i=0, and the smallestEuclidean distance between signal point 825 and a different signalconstellation point associated with an intended signal with i=1. In thisexample, soft bit-metric calculator 360 (FIG. 3) may calculate,

$\begin{matrix}{{{LLR}_{0,{approx}} = {\frac{1}{\sigma_{z}^{2}}\left\lbrack {{D\; 0^{2}} - {D\; 1^{2}}} \right\rbrack}},} & \left( {{EQ}.\mspace{14mu} 14} \right)\end{matrix}$for the least significant bit, i=0. As illustrated in FIG. 8, D1 may bethe Euclidean distance between signal point 825 and signal constellationpoint 810 (e.g., intended symbol “01” and therefore i=1), and D0 may bethe Euclidean distance between signal point 825 and signal constellationpoint 820 (e.g., intended symbol “00” and therefore i=0).

Referring now to FIG. 9, signal constellation set 900 is shown that maybe similar to signal constellation set 800 of FIG. 8, but with differentchannel characteristics. In particular, unlike FIG. 8, FIG. 9illustrates the signal constellation set used by soft bit-metriccalculator 360 of FIG. 3 when the interference channel gain is greaterthan the intended channel gain. In this scenario, the signalconstellation points that correspond to the same intended signal are notdelineated into separate quadrants of the plane. Each quadrant includesa signal constellation point for each of the four possible intendedsignals. Here, signal constellation point 902 corresponds to atransmitted “01” from the intended source and a “00” from theinterfering source.

With noise, the signal that is actually received by mobile stationreceiver 300 (FIG. 3) may have a sampled magnitude and phase at signalpoint 925. The noise that occurred during transmission may account forthe distance, D1, between the expected signal point, signalconstellation point 910, and the actual signal point, signal point 925.Thus, by modeling both the interference signal and the intended signal,the mobile station receiver may accurately and with high confidencedetermine that signal point 925 actually corresponds to signal point910. Soft bit-metric calculator 360 may again compute soft informationaccording to EQ. 14, but where D0 is the Euclidean distance betweensignal point 925 and signal constellation point 920 (e.g., intendedsymbol “00” and therefore i=0), and D1 is the Euclidean distance betweensignal point 925 and signal constellation point 910 (e.g., intendedsymbol “01” and therefore i=1).

Note that, if the interference signal were not modeled and if only thefour signal constellation points corresponding to the intended signalwere used, soft bit-metric calculator 360 would incorrectly concludethat signal point 925 corresponds to signal constellation point 930.That is, of the four signal constellation points that correspond to theintended signal, signal constellation point 930 is closest in Euclideandistance to signal point 925, and therefore soft bit-metric calculator360 can produce soft information that suggests that the transmittedsymbol was “00” instead of “01.” Thus, conventional methods ofcalculating soft information is not capable of producing reliable softinformation in the presence of co-channel interference. On the otherhand, the approach illustrated in FIG. 9 and EQS. 12 and 13advantageously allows a mobile station receiver to accurately decode areceived signal even in the presence of strong interference.

In some embodiments, soft bit-metric calculator 360 of FIG. 3 may use adifferent form of channel information to compute soft information. Forexample, rather than estimating the full channel gain (e.g., magnitudeand phase) of associated with the interfering source, soft bit-metriccalculator 360 may estimate just the magnitude information. For example,soft bit-metric calculator 360 may include computational logic tocompute the average magnitude square, σ_(g) ², or the instantaneouschannel gain, |g_(k)|², of the interference channel. Then softbit-metric calculator 360 may calculate the log-likelihood ratio foreach bit of transmitted information according to

$\begin{matrix}{{LLR}_{i} = {{\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{z}^{2} + {\sigma_{g}^{2}s^{2}}}} \right)}} \right)} - {\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{z}^{2} + {\sigma_{g}^{2}s^{2}}}} \right)}} \right)}}} & \left( {{EQ}.\mspace{14mu} 15} \right)\end{matrix}$when an estimate of the average magnitude square is used, and

$\begin{matrix}{{LLR}_{i} = {{\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{z}^{2} + {\sigma_{g}^{2}{s}^{2}}}} \right)}} \right)} - {\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{z}^{2} + {\sigma_{g}^{2}{s}^{2}}}} \right)}} \right)}}} & \left( {{EQ}.\mspace{14mu} 16} \right)\end{matrix}$when an estimate of the instantaneous channel gain is used. Other LLRequations (or soft information of another type) may be contemplated thatutilize channel information for the interference channels and does notassume that the interference can be modeled as AWGN.

Each of the embodiments described above have been described in terms ofa single interfering source. This, however, is simply to prevent fromovercomplicating the description of the various embodiments. It shouldbe understood that there can be multiple interfering sources. Forexample, referring briefly back to FIG. 1, mobile station 146 may treatboth base stations 122 and 162 as interfering sources. When multipleinterfering sources are present, the above techniques may be extended toincorporate the additional interference signals. For example, the softbit-metric calculator of the mobile station receiver may utilizeadditional signal constellation points when computing the softinformation, where each constellation point is a vector summation ofthree signals—an intended signal and two interference signals.

The mobile station may be able to determine the number of interferingsources to consider. For example, in some embodiments, the mobilestation receiver may attempt to decode control information (e.g., DL-MAPmessage) from any base station, and can consider each base station aninterfering source if the mobile station receiver is able tosuccessfully decode the control information. In some embodiments, themobile station receiver may determine the number of interfering sourcesbased on the number of frequency channels or the frequency reuse of thecommunications protocol. For example, a communications protocol with afrequency reuse of 3 or ⅓ may (e.g., WiMAX) may correspond to a systemwith one intended source and two interfering sources. This is again onescenario illustrated in FIG. 1.

Referring now to FIG. 10A, a flow diagram of illustrative process 1000is shown for computing soft information for an intended signal from areceived signal. The steps of process 1000 may be executed by a receiverto estimate a signal that includes both intended information andinterference information. In particular, these steps may be executed bya receiver implemented on a base station to estimate a signal from anintended mobile station that includes interference from one or moreinterfering mobile stations, or these steps may be executed by areceiver implemented on a mobile station (e.g., mobile station 146 ofFIG. 1) to estimate a signal from an intended base station that includesinterference from one or more interfering base stations.

Process 1000 may have three sub-processes: sub-process 1010, sub-process1020, and sub-process 1030. Sub-process 1010 includes steps that may beexecuted by the receiver to initialize information associated with anintended source, sub-process 1010 includes steps that may be executed bythe receiver to initialize information associated with one or moreinterfering sources, and sub-process 1012 includes steps that may betaken to obtain an estimate of the information from the intended source.

At step 1012 of sub-process 1010, the receiver may receive a pilotsignal from a channel associated with the intended source. Then, at step1014, the receiver may analyze the received pilot signal to estimatechannel information for that channel. For example, using the known pilotvalue transmitted from the intended source, the receiver may analyze themagnitude and phase of the received pilot signal to obtain an estimateof the channel gain. At step 1016, the receiver may perform DL-MAPdecoding or decoding of another form to identify modulation informationassociated with the intended source. For example, the receiver mayidentify the modulation scheme (e.g., QAM, PSK, PAM) used by intendedsource, including the number of signal points and the magnitude/phase ofthe modulation scheme. Thus, steps 1012, 1014, and 1016 allow thereceiver to obtain a full profile and characteristics of the intendedsource and its corresponding channel.

Process 1000 may continue to step 1022 and sub-process 1020 to obtain aprofile and characteristics of each interfering source and channel. Atstep 1022, the receiver may receive a pilot pattern from the channelassociated with a first interfering source, which the receiver mayidentify as being from the first interfering source because of theparticular PN sequence used to transmit the pilot signal. Using theintended value of the pilot signal, the receiver may then analyze thepilot signal at step 1024 to obtain an estimate of channel informationassociated with the channel of the first interfering source. Thereceiver may obtain estimates of, for example, the channel gainassociated with the first interference channel, the average squaredmagnitude of the first interference channel, or the instantaneoussquared magnitude.

At step 1026, the receiver may perform DL-MAP decoding to obtainmodulation information (e.g., modulation scheme and size) associatedwith the first interfering source. The receiver may then repeat thesteps of sub-process 1020 for each remaining interfering source. Forexample, if there are three interfering sources, the receiver canperform steps 1022, 1024, and 1026 three times—one for each interferingsource.

Process 1000 may then continue to the steps of sub-process 1030. At thispoint, the receiver may have relevant information on all the predominantsources that the receiver is within range of. Sub-process 1030 may beginwith step 1032, where the receiver may receive a signal from theintended source that also includes an interference signal from each ofthe one or more interfering sources. The received signal may be a datasignal with network information, for example, and may be transmittedwith a frequency reuse of one. At step 1034, the receiver may computesoft information, such as one or more LLRs, for the intended signalusing the channel information and the modulation information for each ofthe interfering sources. In particular, the receiver may use theinterference channel and modulation information to obtain estimates ofhow the interference signals may affect the intended signal, and maycompute the soft information based on these estimates.

Referring now to FIG. 10B, another flow diagram is shown of anillustrative process for computing soft information for an intendedsignal from a received signal. The steps of process 1040 may be executedby a base station receiver that receives signals which include intendedinformation from an intended mobile station and interference informationfrom one or more interfering mobile stations. Thus, in some embodimentsof the present invention, a base station receiver may compute softinformation using process 1000 of FIG. 10A or using process 1040 of FIG.10B.

Process 1040 may have similar steps to those of process 1000 (FIG. 10A).Therefore, the description of process 1040 will remain brief with theunderstanding that the description of any steps in process 1000 mayapply to corresponding steps in process 1040. Process 1040 differs fromprocess 1000 in the way in which the base station receiver obtains themodulation information that is used to compute soft information. Inparticular, rather than performing DL-MAP decoding based on a DL-MAPmessage, a base station receiver executing the steps of process 1040 canobtain this information through the backbone network at steps 1056 and1066. For example, the base station receiver may obtain modulationinformation for an interfering mobile station by communicating with thebase station whose radio cell the interfering mobile station iscurrently located in.

The steps of processes 1000 and 1040 of FIGS. 10A and 10B are merelyillustrative and represent only some embodiments of the presentinvention. In other embodiments, one or more of the steps in process1000 may be rearranged, combined, removed, or otherwise modified, and/oradditional steps may be added. For example, the steps in sub-processes1010 and 1020 may be rearranged such that the receiver may performDL-MAP decoding to obtain modulation information prior to analyzing thepilot signal to obtain channel information.

Referring now to FIGS. 11-17, various exemplary implementations of thepresent invention are shown.

Referring now to FIG. 11, the present invention can be implemented in ahard disk drive (HDD) 1100. The present invention may implement eitheror both signal processing and/or control circuits, which are generallyidentified in FIG. 11 at 1102. In some implementations, the signalprocessing and/or control circuit 1102 and/or other circuits (not shown)in the HDD 1100 may process data, perform coding and/or encryption,perform calculations, and/or format data that is output to and/orreceived from a magnetic storage medium 1106.

The HDD 1100 may communicate with a host device (not shown) such as acomputer, mobile computing devices such as personal digital assistants,cellular phones, media or MP3 players and the like, and/or other devicesvia one or more wired or wireless communication links 1108. The HDD 1100may be connected to memory 1109 such as random access memory (RAM),nonvolatile memory such as flash memory, read only memory (ROM) and/orother suitable electronic data storage.

Referring now to FIG. 12, the present invention can be implemented in adigital versatile disc (DVD) drive 1110. The present invention mayimplement either or both signal processing and/or control circuits,which are generally identified in FIG. 12 at 1112, and/or mass datastorage 1118 of the DVD drive 1110. The signal processing and/or controlcircuit 1112 and/or other circuits (not shown) in the DVD drive 1110 mayprocess data, perform coding and/or encryption, perform calculations,and/or format data that is read from and/or data written to an opticalstorage medium 1116. In some implementations, the signal processingand/or control circuit 1112 and/or other circuits (not shown) in the DVDdrive 1110 can also perform other functions such as encoding and/ordecoding and/or any other signal processing functions associated with aDVD drive.

The DVD drive 1110 may communicate with an output device (not shown)such as a computer, television or other device via one or more wired orwireless communication links 1117. The DVD drive 1110 may communicatewith mass data storage 1118 that stores data in a nonvolatile manner.The mass data storage 1118 may include a hard disk drive (HDD). The HDDmay have the configuration shown in FIG. 11. The HDD may be a mini HDDthat includes one or more platters having a diameter that is smallerthan approximately 1.8″. The DVD drive 1110 may be connected to memory1119 such as RAM, ROM, nonvolatile memory such as flash memory and/orother suitable electronic data storage.

Referring now to FIG. 13, the present invention can be implemented in ahigh definition television (HDTV) 1120. The present invention mayimplement either or both signal processing and/or control circuits,which are generally identified in FIG. 13 at 1122, a WLAN networkinterface 1129 and/or mass data storage 1127 of the HDTV 1120. The HDTV1120 receives HDTV input signals in either a wired or wireless formatand generates HDTV output signals for a display 1126. In someimplementations, signal processing circuit and/or control circuit 1122and/or other circuits (not shown) of the HDTV 1120 may process data,perform coding and/or encryption, perform calculations, format dataand/or perform any other type of HDTV processing that may be required.

The HDTV 1120 may communicate with mass data storage 1127 that storesdata in a nonvolatile manner such as optical and/or magnetic storagedevices for example hard disk drives and/or DVD drives. At least one HDDmay have the configuration shown in FIG. 11 and/or at least one DVDdrive may have the configuration shown in FIG. 12. The HDD may be a miniHDD that includes one or more platters having a diameter that is smallerthan approximately 1.8″. The HDTV 1120 may be connected to memory 1128such as RAM, ROM, nonvolatile memory such as flash memory and/or othersuitable electronic data storage. The HDTV 1120 also may supportconnections with a WLAN via WLAN network interface 1129.

Referring now to FIG. 14, the present invention implements a controlsystem of a vehicle 1130, a WLAN network interface 1148 and/or mass datastorage 1146 of the vehicle control system. In some implementations, thepresent invention may implement a powertrain control system 1132 thatreceives inputs from one or more sensors such as temperature sensors,pressure sensors, rotational sensors, airflow sensors and/or any othersuitable sensors and/or that generates one or more output controlsignals such as engine operating parameters, transmission operatingparameters, braking parameters, and/or other control signals.

The present invention may also be implemented in other control systems1140 of the vehicle 1130. The control system 1140 may likewise receivesignals from input sensors 1142 and/or output control signals to one ormore output devices 1144. In some implementations, the control system1140 may be part of an anti-lock braking system (ABS), a navigationsystem, a telematics system, a vehicle telematics system, a lanedeparture system, an adaptive cruise control system, a vehicleentertainment system such as a stereo, DVD, compact disc and the like.Still other implementations are contemplated.

The powertrain control system 1132 may communicate with mass datastorage 1146 that stores data in a nonvolatile manner. The mass datastorage 1146 may include optical and/or magnetic storage devices forexample hard disk drives and/or DVD drives. At least one HDD may havethe configuration shown in FIG. 11 and/or at least one DVD drive mayhave the configuration shown in FIG. 12. The HDD may be a mini HDD thatincludes one or more platters having a diameter that is smaller thanapproximately 1.8″. The powertrain control system 1132 may be connectedto memory 1147 such as RAM, ROM, nonvolatile memory such as flash memoryand/or other suitable electronic data storage. The powertrain controlsystem 1132 also may support connections with a WLAN via WLAN networkinterface 1148. The control system 1140 may also include mass datastorage, memory and/or a WLAN network interface (all not shown).

Referring now to FIG. 15, the present invention can be implemented in acellular phone 1150 that may include a cellular antenna 1151. Thepresent invention may implement either or both signal processing and/orcontrol circuits, which are generally identified in FIG. 15 at 1152, aWLAN network interface 1168 and/or mass data storage 1164 of thecellular phone 1150. In some implementations, the cellular phone 1150includes a microphone 1156, an audio output 1158 such as a speakerand/or audio output jack, a display 1160 and/or an input device 1162such as a keypad, pointing device, voice actuation and/or other inputdevice. The signal processing and/or control circuits 1152 and/or othercircuits (not shown) in the cellular phone 1150 may process data,perform coding and/or encryption, perform calculations, format dataand/or perform other cellular phone functions.

The cellular phone 1150 may communicate with mass data storage 1164 thatstores data in a nonvolatile manner such as optical and/or magneticstorage devices for example hard disk drives and/or DVD drives. At leastone HDD may have the configuration shown in FIG. 11 and/or at least oneDVD drive may have the configuration shown in FIG. 12. The HDD may be amini HDD that includes one or more platters having a diameter that issmaller than approximately 1.8″. The cellular phone 1150 may beconnected to memory 1166 such as RAM, ROM, nonvolatile memory such asflash memory and/or other suitable electronic data storage. The cellularphone 1150 also may support connections with a WLAN via WLAN networkinterface 1168.

Referring now to FIG. 16, the present invention can be implemented in aset top box 1180. The present invention may implement either or bothsignal processing and/or control circuits, which are generallyidentified in FIG. 16 at 1184, a WLAN network interface 1196 and/or massdata storage 1190 of the set top box 1180. The set top box 1180 receivessignals from a source such as a broadband source and outputs standardand/or high definition audio/video signals suitable for a display 1188such as a television and/or monitor and/or other video and/or audiooutput devices. The signal processing and/or control circuits 1184and/or other circuits (not shown) of the set top box 1180 may processdata, perform coding and/or encryption, perform calculations, formatdata and/or perform any other set top box function.

The set top box 1180 may communicate with mass data storage 1190 thatstores data in a nonvolatile manner. The mass data storage 1190 mayinclude optical and/or magnetic storage devices for example hard diskdrives and/or DVD drives. At least one HDD may have the configurationshown in FIG. 11 and/or at least one DVD drive may have theconfiguration shown in FIG. 12. The HDD may be a mini HDD that includesone or more platters having a diameter that is smaller thanapproximately 1.8″. The set top box 1180 may be connected to memory 1194such as RAM, ROM, nonvolatile memory such as flash memory and/or othersuitable electronic data storage. The set top box 1180 also may supportconnections with a WLAN via a WLAN network interface 1196.

Referring now to FIG. 17, the present invention can be implemented in amedia player 1200. The present invention may implement either or bothsignal processing and/or control circuits, which are generallyidentified in FIG. 17 at 1204, WLAN network interface 1216 and/or massdata storage 1210 of the media player 1200. In some implementations, themedia player 1200 includes a display 1207 and/or a user input 1208 suchas a keypad, touchpad and the like. In some implementations, the mediaplayer 1200 may employ a graphical user interface (GUI) that typicallyemploys menus, drop down menus, icons and/or a point-and-click interfacevia the display 1207 and/or user input 1208. The media player 1200further includes an audio output 1209 such as a speaker and/or audiooutput jack. The signal processing and/or control circuits 1204 and/orother circuits (not shown) of the media player 1200 may process data,perform coding and/or encryption, perform calculations, format dataand/or perform any other media player function.

The media player 1200 may communicate with mass data storage 1210 thatstores data such as compressed audio and/or video content in anonvolatile manner. In some implementations, the compressed audio filesinclude files that are compliant with MP3 format or other suitablecompressed audio and/or video formats. The mass data storage may includeoptical and/or magnetic storage devices for example hard disk drives HDDand/or DVD drives. At least one HDD may have the configuration shown inFIG. 11 and/or at least one DVD drive may have the configuration shownin FIG. 12. The HDD may be a mini HDD that includes one or more plattershaving a diameter that is smaller than approximately 1.8″. The mediaplayer 1200 may be connected to memory 1214 such as RAM, ROM,nonvolatile memory such as flash memory and/or other suitable electronicdata storage. The media player 1200 also may support connections with aWLAN via WLAN network interface 1216. Still other implementations inaddition to those described above are contemplated.

The foregoing describes systems and methods for computing softinformation at a mobile station to estimate digital information from anintended source. Generally, the invention can be practiced by other thanthe described embodiments, which are presented for the purpose ofillustration rather than of limitation.

What is claimed is:
 1. A method of computing soft information for use inestimating digital information from an intended source, the methodcomprising: determining modulation information associated with at leastone interfering source; estimating intended channel informationassociated with the intended source; determining intended modulationinformation associated with the intended source; receiving a signalcorresponding to the digital information, wherein the signal includes aninterference signal from the at least one interfering source; andcomputing soft information for the digital information from the signalbased on the modulation information, the intended channel informationand the intended modulation information.
 2. The method of claim 1,further comprising: receiving a pilot signal from a channel associatedwith the intended source, wherein estimating the intended channelinformation comprises determining a magnitude and phase of the receivedpilot signal, and wherein determining the intended modulationinformation comprises decoding the received pilot signal to identify amodulation scheme used by the intended source.
 3. The method of claim 1,wherein determining the modulation information comprises: receiving apilot signal from a channel associated with the at least one interferingsource; analyzing a pseudo-noise (PN) sequence associated with the pilotsignal to determine that the pilot signal was transmitted by the atleast one interfering source; and decoding the pilot signal to identifya modulation scheme used by the at least one interfering source.
 4. Themethod of claim 1, wherein determining the modulation informationcomprises: identifying the modulation information from a frequency bandassociated with the at least one interfering source, wherein the signalcorresponding to the digital information uses a larger frequency bandthan the frequency band associated with the at least one interferingsource.
 5. The method of claim 1, wherein determining the modulationinformation comprises: receiving control information associated with theat least one interfering source; and performing DL-MAP decoding on thereceived control information.
 6. The method of claim 1, whereindetermining the modulation information comprises obtaining themodulation information from a backbone network.
 7. The method of claim1, wherein computing soft information comprises computing alog-likelihood ratio (LLR) for an ith bit in the digital informationaccording to:${{LLR}_{i} = {{\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)} - {\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)}}},$where i is an integer, y is the received signal, h is an intendedchannel gain associated with the intended source, g is an interferencechannel gain associated with the at least one interfering source, x isat least a portion of the digital information, l_(i) is a bit positionof the ith bit, k_(i) is a sampling time of the ith bit, s is theinterference signal, and σ_(z) ² is a power of noise.
 8. The method ofclaim 1, wherein computing soft information comprises computing alog-likelihood ratio (LLR) for an ith bit in the digital informationaccording to:${{LLR}_{i} = {\frac{1}{\sigma_{z}^{2}}\left\lbrack {{\min\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2} \right\}} - {\min\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2} \right\}}} \right\rbrack}},$where i is an integer, y is the received signal, h is an intendedchannel gain associated with the intended source, g is an interferencechannel gain associated with the at least one interfering source, x isat least a portion of the digital information, l_(i) is a bit positionof the ith bit, k_(i) is a sampling time of the ith bit, s is theinterference signal, and σ_(z) ² is a power of noise.
 9. The method ofclaim 1, wherein computing soft information comprises computing alog-likelihood ratio (LLR) for an ith bit in the digital informationaccording to:${{LLR}_{i} = {{\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{z}^{2} + {\sigma_{g}^{2}{s}^{2}}}} \right)}} \right)} - {\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}{\exp\left( {- \frac{{{y_{k} - {h_{k_{i}}x}}}^{2}}{\sigma_{z}^{2} + {\sigma_{g}^{2}{s}^{2}}}} \right)}} \right)}}},$where i is an integer, y is the received signal, h is an intendedchannel gain associated with the intended source, σ_(g) ² is an averagemagnitude square of an interference channel gain associated with the atleast one interfering source, x is at least a portion of the digitalinformation, l_(i) is a bit position of the ith bit, k_(i) is a samplingtime of the ith bit, s is the interference signal, and σ_(z) ² is apower of noise.
 10. An apparatus for computing soft information toestimate digital information from an intended source, the apparatuscomprising: computational logic circuitry to: determine modulationinformation associated with at least one interfering source; estimateintended channel information associated with the intended source; anddetermine intended modulation information associated with the intendedsource an input interface to receive a signal corresponding to thedigital information, wherein the signal includes an interference signalfrom the at least one interfering source; and a soft bit-metriccalculator to compute soft information for the digital information fromthe signal based on the modulation information, the intended channelinformation and the intended modulation information.
 11. The apparatusof claim 10, wherein the computational logic is further configured to:estimate the intended channel information by determining a magnitude andphase of a pilot signal received from a channel associated with theintended source; and determine the intended modulation information bydecoding the received pilot signal to identify a modulation scheme usedby the intended source.
 12. The apparatus of claim 10, wherein thecomputational logic is configured to determine modulation informationby: analyzing a pseudo-noise (PN) sequence associated with a pilotsignal to determine that the pilot signal was transmitted by the atleast one interfering source; and decoding the pilot signal to identifya modulation scheme used by the at least one interfering source.
 13. Theapparatus of claim 10, wherein the computational logic is configured todetermine the modulation information by decoding control informationreceived from the at least one interfering source.
 14. The apparatus ofclaim 10, wherein the computational logic is configured to determine themodulation information by obtaining the modulation information from abackbone network.
 15. The apparatus of claim 10, wherein the softbit-metric calculator computes a log-likelihood ratio (LLR) for each bitof the digital information.
 16. The apparatus of claim 15, wherein thesoft bit-metric calculator computes the LLR for an ith bit in thedigital information according to:${{LLR}_{i} = {{\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)} - {\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2}}{\sigma_{z}^{2}}} \right)}} \right)}}},$where i is an integer, y is the received signal, h is an intendedchannel gain associated with the intended source, g is an interferencechannel gain associated with the at least one interfering source, x isat least a portion of the digital information, l_(i) is a bit positionof the ith bit, k_(i) is a sampling time of the ith bit, s is theinterference signal, and σ_(z) ² is a power of noise.
 17. The apparatusof claim 15, wherein the soft bit-metric calculator computes the LLR foran ith bit in the digital information according to:${{LLR}_{i} = {\frac{1}{\sigma_{z}^{2}}\left\lbrack {{\min\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2} \right\}} - {\min\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}\left\{ {{y_{k_{i}} - {h_{k_{i}}x} - {g_{k_{i}}s}}}^{2} \right\}}} \right\rbrack}},$where i is an integer, y is the received signal, h is an intendedchannel gain associated with the intended source, g is an interferencechannel gain associated with the at least one interfering source, x isat least a portion of the digital information, l_(i) is a bit positionof the ith bit, k_(i) is a sampling time of the ith bit, s is theinterference signal, and σ_(z) ² is a power of noise.
 18. The apparatusof claim 15, wherein the soft bit-metric calculator computes the LLR foran ith bit in the digital information according to:${{LLR}_{i} = {{\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(1)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{z}^{2} + {\sigma_{g}^{2}{s}^{2}}}} \right)}} \right)} - {\log\left( {\sum\limits_{{x \in X_{l_{i}}^{(0)}},{s \in S}}{\exp\left( {- \frac{{{y_{k_{i}} - {h_{k_{i}}x}}}^{2}}{\sigma_{z}^{2} + {\sigma_{g}^{2}{s}^{2}}}} \right)}} \right)}}},$where i is an integer, y is the received signal, h is an intendedchannel gain associated with the intended source, σ_(g) ² is an averagemagnitude square of an interference channel gain associated with the atleast one interfering source, x is at least a portion of the digitalinformation, l_(i) is a bit position of the ith bit, k_(i) is a samplingtime of the ith bit, s is the interference signal, and σ_(z) ² is apower of noise.